| bank_loans {ExamPAData} | R Documentation |
Bank Loans
Description
Credit data from UCI Machine Learning Repository.
Usage
bank_loans
Format
data.frame, 41188 observations of 21 variables:
- age
age (numeric).
- job
type of job (categorical.
- marital
marital status (categorical).
- education
'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
- default
has credit in default? (categorical).
- housing
has housing loan? (categorical).
- loan
has personal loan? (categorical).
- contact
contact communication type (categorical).
- month
last contact month of year (categorical).
- day_of_week
last contact day of the week (categorical).
- duration
last contact duration, in seconds (numeric). Important note - this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
- campaign
number of contacts performed during this campaign and for this client (numeric, includes last contact)
- pdays
number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted).
- previous
number of contacts performed before this campaign and for this client (numeric).
- poutcome
outcome of the previous marketing campaign (categorical).
- emp.var.rate
employment variation rate.
- cons.price.idx
consumer price index.
- cons.conf.idx
consumer confidence index.
- euribor3m
euribor 3 month rate.
- nr.employed
number of employees.
- y
has the client subscribed a term deposit?